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As they climb up the maturity curve, manufacturers see a host of benefits as well as challenges with AI, says a new MLC survey.  

KEY TAKEAWAYS:
● 78% of surveyed manufacturers say they plan to increase spending on AI tools in the next two years.
● 46% are already using generative AI tools such as Chat GPT or Microsoft’s Copilot in manufacturing operations.
● 55% expect AI to change the rules of the industry by 2030, despite issues with data and a lack of AI-related workforce skills.  

Manufacturers are planning significant investments in artificial intelligence technologies, including generative AI tools, in the next two years in order to improve their production capabilities, their decision-making processes, and to generate more predictive insights into operations, even as they struggle with data issues and a lack of AI-related skills in their workforces.

Moreover, most manufacturers are moving ahead with AI in a strategic way, with AI closely aligned with their digital transformation and business strategies. And in looking ahead to 2030 and beyond, a majority of manufacturers expect that AI will be a “game-changer” for the industry that will shape the rules of competition for years to come.

These are some of the top-line findings of the Manufacturing Leadership Council’s new survey on AI in manufacturing. The study explored seven major areas of manufacturers’ involvement with AI. These include the maturity level of AI usage, spending intentions on AI tools including GenAI, how companies have organized around the AI opportunity, what benefits manufacturers are looking for from AI, the impact of AI on the workforce, challenges with AI implementations, and the expected future impact of the technology.

Status of AI Adoption and Spending Plans

Over the next 12 to 24 months, 78% of surveyed manufacturers said they plan to increase spending overall on AI tools, with one-fifth expecting to increase their AI investments by more than 30%. Regarding GenAI tools such as Chat GPT or Microsoft’s Copilot, nearly half of survey respondents are currently using these tools in their manufacturing operations and more than 80% said they expect to increase their use in the next two years (Charts 1, 2, 3).

1. Strong Majority to Increase AI Spending

Q: Does your company plan to increase spending on AI tools in the next 12 to 24 months?

2. GenAI Tools Already in Wide Use

Q: Are you currently using GenAI tools such as ChatGPT or Microsoft Copilot in manufacturing operations?

3. More Than 80% Will Increase GenAI Usage

Q: What are your plans for GenAI tools in the next two years?  

Should these investment intentions pan out over the next two years, they will do so against a backdrop of experience with AI that is at an early stage in most companies. Overall, survey respondents indicated that their level of maturity with AI tools in manufacturing operations is nascent (Chart 4). For example, only 5.4% of respondents assessed the maturity of AI tool usage in their manufacturing operations as “advanced”, with 66% indicating it is at an “early” stage and 28% at a “moderate” stage. The findings are similar across 14 other corporate functions, including supply chain, research and development, and quality operations, surveyed by MLC.

This may change as the pace of experience with AI picks up. The adoption of GenAI tools such as Chat GPT and Microsoft Copilot has been remarkable. Currently, 46.7% of survey respondents indicate they are using GenAI tools in knowledge management, to help identify process improvements, in quality operations, and in preventative and predictive plant floor equipment maintenance (Chart 5).

More than one-third of respondents say they plan to substantially increase the use of GenAI tools over the next two years. Another 49% say they are planning a moderate increase in usage of these tools. And a majority, 52.7%, say they will do so according to corporate policies that have been established on the selection and use of GenAI tools (Chart 6).

4. Manufacturing Operations AI Tools are Nascent

Q: Overall, how would you characterize the present maturity of artificial intelligence usage in your company’s manufacturing operations? (on a scale of 1-10, with 10 being the highest level of maturity) 

5. Knowledge Management is Primary Area of GenAI Usage

Q: If yes, in which areas have you implemented generative AI? (top 3)

6. A Majority Have a Corporate Policy on GenAI

Q: Has your company established a corporate policy on the selection and use of GenAI tools?  

AI Strategy and Organization

Perhaps a result of many years of using traditional AI tools such as business intelligence and machine learning technologies, most manufacturers say their companies have a corporate AI strategy (Chart 7). Moreover, 78% say their AI initiatives within manufacturing operations are part of their company’s larger digital transformation and business strategies (Chart 8).

In addition, a substantial percentage of survey respondents, 42.8%, say that their company’s AI governance process is part of their overall data governance strategy. Nearly 20% indicate that they have an AI governance strategy but that it is not part of data strategy, while 27% say their companies do not have an AI governance strategy at all.

But when it comes to being able to identify who or what corporate unit oversees AI initiatives, the lines are blurry. The chief information officer was cited by just over 21% of respondents as the corporate officer in charge of AI initiatives, but an equal number of respondents say that authority is unclear in their companies. Given the proliferation of technology-oriented executive titles and functions in recent years, it is perhaps not surprising that involvement and even responsibility for AI projects has crossed organizational boundaries in many companies (Chart 9).

7. Manufacturers Are Thinking Strategically About AI

Q: Does your company have a corporate AI strategy?

8. Digital, AI Linked in Vast Majority of Companies

Q: Are your AI initiatives within manufacturing operations part of a larger digital transformation strategy for your company? (select one)

9. The CIO is Most Often in Charge of AI

Q: Organizationally, who or what department is in charge of AI initiatives in your company?

Expected Benefits of AI

At the end of the day, what benefits are manufacturing companies looking to get out of their investments in AI? The answers are to be found in the rapidly increasing volumes of data companies are generating from their extensively connected businesses.

When asked to assess a list of 11 potential business benefits using a low/moderate/high scale, the three potential benefits that motivated a strong majority of respondents for their “high” potential were more predictive insights from data, better decision making, and better planning (Chart 10).

In operations, the three potential benefits scoring a “high” ranking were improved predictive maintenance, increased uptime of factory assets, and a more efficient use of the workforce. In the supply chain category, the three were better supply chain planning, more predictive insights, and increased supply chain agility (Chart 11).

Aspirations aside, one of the disciplines that manufacturers will have to get better at as their experience with AI matures is measuring effectiveness. Currently, 66% of respondents say their companies do not have a specific set of metrics to measure the effectiveness and impact of AI implementations (Chart 12).

Although the prospect of AI-supported autonomous factory and plant operations is being widely discussed in the industry today, survey respondents take a nuanced view of the concept. The idea of “fully autonomous” operations is a foreign one, but noteworthy percentages of respondents do expect a substantial degree of autonomy to be achieved in their plants and factories in the distant future (Chart 13).

10. Better Insights, Decision Making Are Top Business Benefits

Q: How would you assess the potential business benefits of AI in your company ? ( top 3 benefits ranked by highest level of response)

11. Maintenance, Uptime Seen as Operational Benefits

Q: How would you assess the potential benefits of AI in manufacturing operations? ( top 3 benefits ranked by highest level of response )

12. Most Do Not Have AI Metrics

Q: Do you have a specific set of metrics to measure the effectiveness and impact of AI implementations?

13. A Majority Believes that Autonomous Plants Are in the Distant Future

Q: What statement would best describe your expectation about the future state of factories and plants as a result of the use of AI by 2030?

AI’s Effect on the Manufacturing Workforce

As other MLC studies have previously indicated, manufacturers are largely not buying into the fear that AI adoption will result in widespread worker displacement. Nearly one half of survey respondents, 47%, do not expect their company’s workforce headcount to be affected by AI. However, just over one third, 36.4%, do expect that headcount levels will decrease, and seven percent expect that headcount will increase because of AI adoption (Chart 14).

For those expecting some workforce displacement, 56% say that those displaced will be retrained or reassigned to one degree or another, with 22% saying that 20% of more of those displaced will be offered other opportunities.

14. Nearly a Majority See No AI Effect on Workforce Levels

Q: What impact do you think AI will have on your workforce headcount by 2030?

AI Challenges, Policy Considerations, and Future Impact

As it is with any IT or OT technology, there are always challenges associated with their implementation and use. By far the most significant challenge with AI has to do with data, say 68% of survey respondents, with data quality, validation, and contextualization at the top of their punch lists (Chart 15, 16). The lack of AI-related skills in the workforce and understanding the business case for AI were also cited as key challenges.

Although it did not make the top three challenges indicated by survey respondents, embedded bias in algorithms was cited by one-fifth of survey takers as an issue. Misinformation was also cited as a key risk factor (Chart 17).

On the question of whether the U.S. should have a federal-level industrial policy on AI, nearly half of survey respondents are in favor of such a policy. Furthermore, nearly half also support regulation of AI by the federal government (Charts 18,19).

And, for the first time on a question that has been asked in previous MLC surveys, a majority of respondents now feel that AI will be a “game-changer” for the industry in the future (Chart 20).

Just how fast that future could arrive will no doubt be on the minds of manufacturing executives as they think about how to remain competitive in the years ahead.

15. Data Issues Are the Biggest Challenge with AI

Q: What do you see as the biggest challenges to AI adoption and use?

16. Data Quality, Contextualization Are Top Challenges with AI Data

Q: What areas of working with AI-related data are proving most challenging?

17. Misinformation is Seen as the Biggest Risk with AI

Q: What do you see as the most significant risk in using AI?

18. Nearly a Majority Are in Favor of a Federal AI Policy

Q: Should the U.S. have a federal-level industrial policy to encourage AI development and adoption?

19. Nearly Half Favor Federal Regulation of AI

Q: Do you think AI should be regulated by either the states or the federal government? (select one)

20. Most See AI as A Game-Changer for the Industry

Q: Ultimately, how significant an impact will AI have on the industry by 2030 and beyond?

About the author:

David R. Brousell is the Founder, Vice President and Executive Director, Manufacturing Leadership Council

Demystifying AI and ML to uncover seamless integrations to improve planning, manage disruptions, and enhance operational efficiency

TAKEAWAYS:
● AI and ML significantly improve production execution, predictive maintenance, and scheduling, leading to higher quality, reduced downtime, and optimized operations.
● Successful AI/ML integration requires robust data management, cross-functional collaboration, scalable pilot projects, continuous performance monitoring, and staff training.
● Emerging technologies like Generative AI, synthetic data, and autonomic systems promise to revolutionize manufacturing with self-optimizing operations and advanced model training techniques.   

AI Manufacturing Use Cases

There are many different application areas for AI across manufacturing and production. Even limiting the scope to just “within the four walls of the shop floor,” one could write a novel on all the possibilities (let alone thinking about supply chain and beyond).

These three use cases represent some of the most typical and prominent opportunities for a manufacturing leader.

1. Production Execution – For example, AI-driven quality control systems inspecting products in real-time, ensuring consistent quality and minimizing waste.
2. Predictive Maintenance – For example, AI continuously monitoring equipment health and ML predicting potential failures, together with the goal of reducing downtime and maintenance costs.
3. Production Scheduling – For example, AI optimization creating constraint based schedules and ML analyzing historical schedule performance to drive increased throughput and resource utilization.

Each of these use-cases have typically been managed by spreadsheets, legacy systems or just “tribal knowledge” in the past, but today’s manufacturing and product complexity means that only AI and ML algorithms can now effectively process and analyze the vast amounts of production data in real-time, to help humans make confident and effective plans and decisions.

1.    Optimizing Production Execution with AI and ML

Even simple manufacturing operations generate tremendous amounts of data. Although still a challenge, the easier part of the task is to collect the data from the various sensors and machinery across the shop floor. Once collected, manufacturers can start to leverage AI’s power.

The first, immediate, benefit is visualization of the data collected. For humans, that can be helpful if there are obvious anomalies in what is seen, but most manufacturing problems have root causes that run deeper than obvious and isolated deviations. For example, product defects that present as a lack of worker training but are actually defects related to raw material quality.

AI can efficiently identify anomalies in real-time as the data is collected, and then correlate the data with upstream and downstream processes to identify associated processes that might show causation between the processes and therefore identify the root cause. Doing this in real-time allows immediate adjustment to production runs (especially in batch environments) that can prevent wasted time and material.

“By reducing unplanned downtime, manufacturers can extend equipment lifespan and increase overall production efficiency at reduced costs.”

Because this data is continuously collected, ML can be applied to the historical data store to reveal additional insights and patterns that can help drive future improvements. For example, ML can analyze quality data to identify subtle trends in material or production tolerances that may be leading to failures that are more critical. It could also predict and optimize energy consumption dynamically, which can lead to more sustainable production practices.

Taken a step further, AI and ML could be entrusted to not only identify current and future problems, but to autonomously adjust production parameters in real-time to optimize performance and respond to changing conditions without the need for human intervention. This, of course, requires a certain level of trust between man and machine – more on that in a moment.

2.    Predictive Maintenance Using AI and ML

Despite the best production control and execution, one challenge that every manufacturer deals with is machine maintenance – both planned and (more challenging) unplanned. Just as AI and ML can optimize product quality, the same principles can apply to production resources.

Predicting maintenance problems mitigates the disruption that they cause. Continuous monitoring of equipment performance and health via sensors provides a wealth of data that can feed ML algorithms. These algorithms are adept at developing insights into the probability of unplanned downtime based upon historical frequency and patterns of past machine performance. This means manufacturers will be able to see predictive alerts that can then use to prepare for potential problems: either pro-actively accelerating scheduled maintenance or planning for additional capacity.

Machine failures not only affect finished products but also the asset intensive machine equipment. By reducing unplanned downtime, manufacturers can extend equipment lifespan and increase overall production efficiency at reduced costs.

Does AI (optimization) play a role in predictive maintenance? Not as much in the prediction or prevention of downtime, but it certainly is the hero when disruption hits the production line.

3.    AI and ML Applications in Production Scheduling

The famous statistician W. Edwards Deming said, “Uncontrolled variation is the enemy of quality.” This is absolutely the case in manufacturing, and manufacturers can easily add “efficiency,” “cost” and several other nouns to “quality.” One of the key Industry 4.0 attributes that companies are seeking is “agility” – the ability to react to change as it happens with confident decision-making. AI (specifically optimization) can be applied to this requirement with great effect.

Even simple manufacturing operations contain too many constraints and variables to allow for manual scheduling of jobs with any level of precision. Spreadsheets and manual scheduling simply are not capable of optimizing production against operational goals, let alone providing decision support for possible alternative plans. AI optimization excels at being able to consider millions of potential combinations of material, capacity and skill-based constraints while searching for plans that meet stated KPIs, such as order fulfillment, changeovers, etc.

This ability to create an optimal production schedule in seconds (compared to hours or days) means manufacturers can instantly react to changes in orders, capacity or material. Additionally, they can experiment with multiple “what-if?” scenarios to evaluate trade-offs and opportunities to meet more corporate goals.

ML also plays a role in this area. Since no plan survives contact with the real world, it is inevitable that unforeseen changes will affect even the most promising plans. In the same way that historical machine data can be reviewed to predict failures, ML can mine the performance of previous plans to analyze whether there are patterns that reveal how future schedules can be additionally adjusted to improve their effectiveness.

Implementing AI and ML Solutions in Manufacturing

There are lots of use-cases and lots of benefits, but where and how to start? The most obvious common foundation might be the reliance of these technologies on data.

AI and especially ML, require large amounts of accurate data (and in real-time in many cases). Therefore, manufacturers will need to implement robust data collection and management practices to ensure that the data feeding into AI/ML models is accurate and reliable. This might also mean a strategy and investment into 5G and IIoT, since the data will be coming from sensors and IoT devices that collect real-time data from production equipment.

Beyond simply collecting the data, several other typical project items also deserve consideration:

1. Foster collaboration between IT, data science, and operational teams to ensure alignment and effective implementation of AI and ML solutions. Example: Create cross-functional teams comprising members from manufacturing, IT, and data science to oversee the AI/ML integration process.
2. Begin with small-scale pilot projects to test the feasibility and impact of AI/ML solutions before full-scale implementation. This approach helps manage risks and identifies potential issues early. Example: Implement a pilot project for predictive maintenance on a few critical machines to validate the technology’s effectiveness.
3. Design AI/ML solutions with scalability in mind, allowing for easy expansion as the technology proves its value. Example: Develop modular AI/ML solutions that can scale across different production lines or facilities as needed.
4. Regularly monitor the performance of AI/ML models and make necessary adjustments to improve accuracy and efficiency. Example: Implement a feedback loop to continuously assess model performance, and make improvements based on real-world data.
5. Provide training for staff to understand and work with AI/ML technologies, ensuring they are equipped to leverage these tools effectively. Example: Conduct workshops and training sessions to upskill employees in AI/ML concepts and tools.
6. Consider AI & ML “as a service.” Vendors are now starting to offer MLaaS to manufacturers that do not have the required skillset or budget to implement a long-term strategy, yet need an answer to a specific problem or challenge. By contracting with a vendor for an “outcome,” they can leverage AI or ML to solve key strategic problems while offsetting the cost of software licenses, IT overhead and additional skilled staff.

This last item has an underlying issue that is reminiscent of the continuing references to the “Skynet” of Terminator movie fame: trust in AI. While it is unlikely that a production scheduler is going to trigger the “rise of the machines,” the general complexity of AI/ML, data, and the scope of application means that much of the actual workings of the algorithms are difficult, if not impossible to comprehend. In other words, the output, or “what,” is understandable but the “why” is often not. In fact, the output may even seem counter-intuitive to one’s own gut-feel. Sometimes, this is because the technology is sacrificing particular operational metrics to achieve a greater goal.

For example, an optimized production schedule might meet the primary KPI of “order fulfillment” at the expense of OEE for particular resources. On the shop floor, a human scheduler might see this as a flaw in the plan and attempt to “tweak” the schedule to keep the resource loaded when, in fact, this will affect the plan in negative ways and reduce overall business objectives.

This “trust in AI” is something that will certainly change over time. In the meantime, you can add one additional practical step to the project plan to help:

• Communicate the benefits and potential of AI/ML solutions to all stakeholders to mitigate resistance and encourage adoption. Example: Hold informational sessions and provide case studies demonstrating the positive impact of AI/ML on similar manufacturing operations.

Is it Worth It?

There is obviously some effort and commitment required to implement AI and ML into manufacturing processes but the value is certainly there. By leveraging AI and ML, manufacturers can achieve greater operational agility, reduced costs, and improved decision-making capabilities, positioning themselves competitively in an increasingly complex and dynamic market landscape.

Manufacturers are seeing critical KPIs affected very significantly in the following areas:

1. Cost Reduction: In both production efficiency and quality-related costs, improvements up to 50% are possible
2. Productivity: By replacing manual decisions with automation, planners are recovering 25-50% of the time for more value-added activities
3. Time to Market: More efficient scheduling and execution can accelerate time to market (especially in fast moving sectors) by up to 40%

In addition, the benefits related to equipment maintenance and lifespan can have a significant positive impact on capital expense budgets.

Mileage may vary, of course, so another critical step in the plan is to build a solid business case based upon clearly stated goals and expectations with a realistic state of as-is and to-be. The technology vendors who provide AI and ML are well aware of the capability and complexity of their wares, and so should be willing to work with manufacturers on this step (it is in their best interest after all to ensure a successful long-term partnership).

“Spreadsheets and manual scheduling simply are not capable of optimizing production against operational goals, let alone providing decision support for possible alternative plans.”

Whether manufacturers leverage a technology vendor, consulting partner, or has the expertise in house, they should not skip this step. Even if their executive team has a pot of money and an urgent decree to get on the AI and ML bandwagon, it is imperative to understand why this technology is needed and exactly what element of the possible capabilities will be critical to achieving success and how.

Future Trends and Innovations

The integration of AI and ML into an Industry 4.0 strategy underscores a commitment to innovation and continuous improvement, setting the stage for more sustainable and resilient manufacturing practices. Manufacturers can achieve effective use of AI today (and many companies are already receiving the benefits). But, what of the future? What can we expect from AI in the next decade and beyond?

It would be easy to say, “The possibilities are endless,” and they probably are in many respects. What seemed impossible yesterday is in practice in manufacturing today. Here are some thoughts about what might be possible tomorrow:

• Generative AI for Production Optimization: While this article did not focus on the sub-genre of Generative AI, it has the potential to revolutionize manufacturing by providing powerful tools to optimize production processes. Imagine GenAI developing completely new manufacturing lines or optimization strategies that are not immediately apparent to human operators.
• Synthetic Data for Enhanced Model Training: The use of synthetic data is gaining traction as a method to train AI models without compromising privacy or requiring extensive real-world data collection. Synthetic data can simulate a wide range of operational conditions, enabling robust training of ML models for applications such as predictive maintenance, demand forecasting, and process optimization.
• Autonomic Systems for Self-Optimizing Operations: Autonomic systems utilize AI to automatically adjust and optimize processes without human intervention. These systems continuously learn from operational data, making real-time adjustments to maintain optimal performance. Applications include dynamic scheduling, resource allocation, and process optimization.

It may seem that AI’s evolution will outpace manufacturers’ ability to implement any of its capabilities. This pace of innovation also often causes internal initiatives to move so fast that they can fail. Although AI and ML can be applied to the most complex and strategic processes, they can also be applied to solve very simple and tactical challenges – production scheduling is a perfect example of this. It is a classic situation of not suffering from “analysis paralysis.” In fact, here are four activities every manufacturer could benefit from now:

1. Become educated on AI and ML’s fundamentals: it is a large discipline across many industries. Leaders should become confident and articulate on the basic capabilities and how they could be leveraged in manufacturing scenarios.
2. Don’t get caught up in the hype: for those just starting out in AI, find the practical use-cases and always work backwards from the business problem, not forwards from the technology capabilities.
3. Learn from all voices: there are fast-growing practical experiences in manufacturing and many third party sources and technology experts who are publishing prolifically on this sector.
4. Finally, leverage the extensive network and information provided by the MLC and its members. There are extensive resources and experiences just a mouse-click away.  M

About the author:

Adrian Wood is Director of Strategy & Marketing at Dassault Systèmes

To capitalize on AI, companies need to build a strong data foundation and upskill their workforce with AI skills. 

TAKEAWAYS:
● Manufacturers cannot afford a wait-and-see approach–they need to explore where AI adoption will provide the most tangible and immediate benefits.
● AI can increase the efficiency of administrative tasks, predict demand patterns, and improve inventory planning.
● Companies will need to upskill their existing teams to succeed in a data-driven environment and maximize new AI-enabled capabilities.  

Artificial intelligence (AI) has the potential to transform every aspect of a manufacturer’s business—but some of its greatest impact will be on the supply chain.

Supply chain professionals will be able to enhance their work with the insights that AI provides, allowing them to bring together data from around the business in real time to make data-driven decisions and uncover opportunities to mitigate risk and improve resilience. They can also use AI to increase the efficiency of administrative tasks, predict demand patterns, improve inventory planning, and much more. Many supply chain professionals will soon interact with AI for the first time in their careers as manufacturers seek to increase their AI and machine learning (ML) investments. In fact, BDO’s 2024 Manufacturing CFO Outlook Survey found that 47 percent of chief financial officers (CFOs) are increasing investment in AI and ML this year.

While AI is promising, manufacturers need to build the foundation for adoption; otherwise, their investments will not realize expected return on investment (ROI). How can manufacturers prepare their supply chain management teams for AI adoption?

In this article, we explore steps manufacturing leaders should take to enable successful AI adoption in the supply chain function:

• Build a strong data foundation
• Enable cross-functional collaboration
• Foster AI-related skills in the supply chain workforce
• Create a culture of curiosity around AI usage

Build a Strong Foundation

The first step in any organization’s AI journey is to build a strong data foundation. This involves consolidating disparate data sources, ensuring all data are stored in an accessible location with appropriate reference fields enabling analysis across datasets, and implementing strong data governance standards and processes. Digitally mature manufacturers may already have this data infrastructure in place; however, many manufacturers’ existing data management practices are insufficient to support many of the most promising AI use cases.

To build this foundation, manufacturers need AI-savvy data scientists to help them interrogate and analyze data to extract useful insights. Manufacturers can either hire data scientists directly or work closely with a third-party provider experienced in helping companies set up their data infrastructure.

“Many supply chain professionals will soon interact with AI for the first time in their careers as manufacturers seek to increase their AI and machine learning (ML) investments.”

Once an organization has data scientists onboard, it should collaborate with operations, supply chain, quality, and other leaders to identify the business problems it is trying to solve and the relevant internal and external data that will power its AI tools. Once these are identified, the organization can begin designing the company’s AI-enabled strategy.

Enhancing Cross-functional Collaboration

To deliver the most value, AI requires access to data from across the organization. Enabling this kind of data sharing requires the integration of many disparate systems—including warehouse management systems (WMS), customer relationship management systems (CRM), supplier relationship management systems (SRM), and enterprise resource planning systems (ERP).

The supply function is an ideal area to roll out new data-sharing processes, as supply teams already naturally interact with groups across organizations. Supply leaders can be critical partners to data scientists and other individuals leading AI adoption and setting up new tools and processes. Strong data governance is also critical to ensure that AI tools provide accurate outputs based on high-quality, reliable data sets.

Effective data sharing across systems can provide supply chain professionals with real-time, organization-wide visibility. Powered by AI, supply chain professionals can have quick access to aggregated insights that enhance their decision-making and free them up from having to perform manual analysis. For example, a procurement professional at an appliance manufacturer may receive an automated alert from an AI-powered tool that there has been an influx of negative customer feedback pouring into its CRM system due to unreliable electric motors in some washing machines. Since the CRM system shares data with the company’s SRM system, the procurement professional can identify the relevant supplier and reach out to discuss how to alleviate the issues. The company could also use this information to inform benchmarks for supplier performance.

Fostering New Skills

While many supply chain organizations will need to hire professionals with knowledge of strong data governance principles and an understanding of how large language models and other AI solutions work, the larger challenge will be upskilling their existing teams to succeed in a data-driven environment and optimally leverage the company’s new AI-enabled capabilities.

In a world where machines can automate tasks, perform rapid calculations, and analyze vast data sets to uncover deep connections and patterns, leading supply chain organizations are prioritizing analytical thinking and digital dexterity—that is, the ability of employees to adopt and adapt to using emerging technologies to deliver improved business results—as part of their core curriculum to upskill supply chain teams. Training supply chain professionals to use generative AI tools is also essential. For example, if a manufacturer is deploying generative AI tools, training on prompt engineering—that is, how to design effective queries to extract the necessary data from AI tools in a useful format—will be vital.

“Starting with a small pilot project focused on achieving tangible, near-term ROI can help get team members on board and establish internal AI champions.”

Application-based instruction that leverages a controlled AI environment that is disconnected from the company’s production systems will be critical to building these skills. The test environment can also teach employees about their company’s acceptable use policies for AI and provide a safe place for professionals to learn from mistakes.

Many companies are also investing in third-party developed prompt libraries or guides for sample queries to run in specific scenarios to support increased user adoption. For example, an advanced inventory planning tool might recommend questions like “identify which suppliers have had lead times that were more than five days past system projections over the past three months.”

Creating a Curious Culture

Successful AI implementation requires empowering individuals to use AI and ML tools in their everyday work. To encourage adoption, manufacturers need to foster a culture of curiosity by training and inspiring their teams to explore the possibilities that AI tools can provide. This culture can also help manufacturers overcome common roadblocks to adoption.

For example, some supply chain professionals may worry that AI will replace them or will complicate their jobs. Explaining how AI can augment (versus replace) human expertise and judgment is essential to overcoming these hurdles. Beyond mitigating replacement concerns, manufacturers can demonstrate the value that achieving mastery over AI tools and skills can have for their employees’ professional development.

Starting with a small pilot project focused on achieving tangible, near-term ROI can help get team members on board and establish internal AI champions. For instance, a manufacturer that has access to an internal generative AI tool could work closely with procurement teams to show how it could support researching new vendors or generating information that may be helpful in a negotiation.

The Future Won’t Wait

AI is no longer on the horizon—it’s here, and leading manufacturers are moving quickly to explore how AI-driven solutions can enhance productivity, quality, and safety while making companies more resilient and cost-effective.

Manufacturers who want to remain competitive can’t adopt a wait-and-see approach. Instead, they need to start preparing their organizations for AI by establishing their data infrastructure, equipping their teams with the necessary skills, while also exploring where AI adoption will provide the most tangible and immediate benefits. Those initial wins can then be scaled into broader solutions that create a long-term competitive advantage.  M

About the authors:

Jim Blackwell is market leader at BDO Digital.

Maurice Liddell is manufacturing market leader at BDO Digital.

R. J. Romano is supply chain managing director at BDO USA.

Manufacturers have a treasure trove of data that GenAI can use to enhance performance, agility, and growth.

TAKEAWAYS:
● By combining actual historical product data and simulated insights, manufacturers can unlock new levels of innovation and competitiveness.
● More manufacturers are harnessing GenAI—Eaton, for example, is using the technology to cut product design time by nearly 90 percent.
● Manufacturers that combine robust data sets with clear GenAI use cases are well-positioned to harness the transformative power of GenAI.   

Manufacturers are already seeing glimpses of how artificial intelligence (AI) is reshaping the industry. Applying AI to R&D/product design can have a force multiplier effect across the entire product development lifecycle. Companies can use generative AI (gen AI) to develop new products at lightning speed that are already optimized for cost, carbon, performance, and even factory location. To illustrate the focus in this area, Bain & Company¹ reports that 75 percent of manufacturers surveyed list AI and related technologies as their top engineering and R&D priority.

But missteps today could leave companies adrift amid the AI sea change and unable to navigate new market realities. Proactive manufacturers are addressing how gen AI capabilities differ from traditional AI, are defining specific gen AI use cases for their needs, and are taking steps to generate value from this new technology.

What is Gen AI?

Generative AI represents a new frontier in AI (ChatGPT may be the most well-known example). Traditional AI, also known as deterministic AI, applies pre-programmed rules and algorithms to make decisions. Traditional AI systems solve well-defined problems—for example, determining the most effective manufacturing process based on the properties of a specific part—and perform repetitive tasks.

Instead of using predetermined rules, gen AI identifies data patterns to create new, unique content. This requires accurate data, machine learning (ML) for powerful analysis, and large multimodal models (LMMs) to process and generate information across multiple formats, including text, images, and video.

Data Quality: The Launchpad for AI Innovation

Financial services and other industries are awash in data because different industry sub-segments—such as consumer banking and asset management—can use similar data sets to create AI models for automated customer service and other applications. But that scale doesn’t apply to manufacturing because many sectors have different operating models (think high-volume consumer electronics manufacturing vs. low-volume products for aerospace or other highly regulated industries).

To compensate, manufacturers are combining actual historical product data and simulated insights to provide the data volume and quality required to make informed decisions.

How to Capitalize on Actual Historical Information and Simulated Design Data

A manufacturer’s actual historical data can include design files of its popular products, a list of its highest-margin products, product costs, production volumes, and preferred supplier information. It can also feature detailed performance data regarding company-owned factories and/or production lines.

However, analyzing a manufacturer’s actual historical data doesn’t typically help to identify areas for cost or time savings. For example, how do manufacturers know if they’re overpaying for a component if they only have quote and payment information?

To gain these types of insights, manufacturers rely on simulation and modeling to identify opportunities for improvement across the organization—including design for manufacturing (DFM), cost modeling, sustainability insights, and structural performance (FEA analysis). These simulation applications provide additional analysis and guidance to optimize an array of variables.

“Missteps today could leave companies adrift amid the AI sea change and unable to navigate new market realities.”

Integration across applications and platforms is central to harnessing all manufacturing information effectively. With complete control over product data, manufacturers can instantly adjust shop floor labor rates for a plant in Taiwan or update raw material cost data to reflect inflationary pressures.

Manufacturers who understand why there are discrepancies between actual historical data and insights from simulation applications can use this knowledge to build precise AI models based on the most accurate information and establish parameters from multiple types of data.

Eaton Spotlights the Power of Gen AI

Eaton is a $23 billion intelligent power management solutions provider for industrial and manufacturing industries. Customers regularly require customized Eaton components/products for their new product development initiatives, which can range from passenger car valve stems to lighting fixtures.

Due to technical complexity, it can take Eaton months to complete a manual product design. For example, a lighting fixture design can require input from thermal, electrical, mechanical, optical, and manufacturing engineering.

“Eaton’s vision is to take our traditional design processes from months to minutes,” said Uyiosa Abusomwan, senior global technology manager of Digital Design and Engineering at Eaton.

Eaton’s gen AI capability is built on a robust set of actual historical product design data and insights from the company’s simulation software portfolio—including aPriori for cost modeling, DFM, and sourcing. Eaton combines this information to create detailed model-based design specifications and properties to support its gen AI development.

With gen AI, Eaton runs thousands of design iterations in minutes (or less) and proposes the top five designs. Once the designs are fed through a high-fidelity simulation, Eaton’s digital design and engineering team conducts a detailed review. This workflow empowers the Eaton engineering team to review AI outputs for product validation and quality control, and to streamline decision-making.

Result: Eaton Cuts New Product Design from Months to Minutes

Eaton’s impressive results from its high-fidelity gen AI initiative include the following:

• Minimizing the weight of a liquid-to-air heat exchanger by 80 percent
• Lowering the design time for a high-speed gear by 65 percent
• Reducing the design time for an automated lighting fixture by 87 percent

Eaton’s gen AI capabilities support the company’s goal to scale new product development and accelerate time-to-market to address customer needs. The technology could also support the company’s goal to become carbon neutral by 2030.

Take the Next Step In Your Gen AI Strategy

Despite some early forays into gen AI, manufacturers are still primarily laying the groundwork in this area. The MLC’s “Future of Industrial AI in Manufacturing” survey reports that 28 percent of respondents have gen AI projects that “passed the pilot stage.” What’s revealing is that more than half of those surveyed aren’t incorporating gen AI into their digital transformation strategies, and nearly two-thirds aren’t measuring the impact of their AI investments.

Gen AI technologies continue to evolve rapidly in this dynamic field. Given the pace of innovation, it’s hard to predict what AI algorithms will be capable of during the next few years. However, the need for accurate, robust data are a constant pillar for AI and other business-critical operations.

Today, companies have a wealth of information to harness for short-term gains and long-term success: traditional AI engines that power product design, sourcing, and digital factory simulation—along with their actual product data. Gen AI is uniquely positioned to transform the entire product development lifecycle. Manufacturers that act quickly and strategically are well-positioned to gain new levels of performance, growth, and agility.  M

About the author:

Philippe Adam is the chief marketing officer at aPriori.

References:
1.     Bain & Company. “Bain’s Global Machinery & Equipment Report 2024.” 2024.
2.     Manufacturing Leadership Council. The Future of Industrial AI in Manufacturing. 2023.

Generative AI is increasing the potential for data to fundamentally change the way manufacturers operate throughout the manufacturing value chain — and bring significant value for the workforce.  

TAKEAWAYS:
● Data currently is seriously underused at the operational level, leading to wasted potential for performance improvement.
● GenAI can empower worker efficiency and effectiveness — if they use it correctly. The key is focusing on how AI can augment workers’ expertise and make them more efficient and productive.
● Most manufacturers don’t collect and retain the data needed to benefit from AI. This is a good place to start.

In the Manufacturing Leadership Council’s study, Manufacturers Go All-In on AI (October 2023), nearly half of executives cited AI/machine learning (ML) as the technology they expect to have the most future impact on manufacturing operations — more than any other mentioned. Almost half — 47% — expect it to be a game changer by 2030. In Rockwell Automation’s State of Smart Manufacturing Report, generative AI was the No. 1 area for technology investment in the next 12 months, and 83% of those polled anticipate using it in 2024.

West Monroe’s survey of mid-sized manufacturers, The State of Manufacturing, shows companies are realizing the benefits from AI and ML, and increasingly infusing data into their operations. But one finding stood out: While 84% of companies surveyed use data extensively for decision-making at the executive level, that does not carry to other levels. Only 38% of middle management uses data extensively, and 47% of operational staff rarely use data.

Think about the wasted performance potential that statistic implies. Leveraging data in real time on the operations floor can help employees think more strategically, make informed decisions that reduce costs and improve margins, and drive businesses forward. That’s where AI comes in. It harnesses exponential volumes of data currently going unused to improve manufacturing operations — putting insight in the hands workers that makes them more effective and efficient.

But first, workers need to become comfortable with AI. Real or not, perceptions abound that AI will replace human work and jobs. In The Future of Industrial AI in Manufacturing, executives were mixed on this point. Nearly half (45%) said they don’t expect an impact on the workforce. But a sizeable minority, 21%, do see it decreasing the size of the workforce.

To unlock value for the workforce, manufacturers should be focusing on AI as a way to transfer knowledge “within the four walls” and augment workers’ expertise—empowering them perform day-to-day responsibilities better and more efficiently. Following are some principles for doing so.

Ensure Employees Are Using AI Right 

According to Microsoft, 75% of knowledge workers are already using GenAI at work. But in our opinion, many are using it wrong. They are defining their own tools and approaches, without sufficient monitoring or governance. It’s up to leadership to convince employees to use it the way the company wants. Understand that guiding appropriate GenAI adoption requires:

• Both a top-down and bottom-up strategy
• More of a cultural movement and less of a mandate
• Trust and mentorship
• Success metrics defined on both at the company and individual level

Educate Everyone — Continuously

Because AI is a rapidly evolving discipline, education isn’t a one-and-done project. It requires continuous focus and effort. And it involves everyone—from senior executives to the shop floor. Seek to learn everything you can about the fundamentals of large language models (LLMs), the options, and the skills required. Don’t just read about it. Engage with manufacturing peers or other organizations to share experiences and points of view.

Given the buzz around GenAI, it is particularly important to understand the differences between this form of AI and the broader concept of AI/ML. In a mature state, both AI/ML and GenAI may play roles in optimizing manufacturing operations. A good example is machine maintenance. By putting sensors on equipment, you can use analytics to predict when the machine will need maintenance. That is AI/ML. When the technician is performing maintenance has questions, GenAI can provide answers rapidly, in an easily understandable format.

Pursue the Right Use Cases

We see many organizations trying to explore as many use cases as possible rather than focusing on a handful of the most promising ones. Casting a wide net is a good starting point, but we guide clients to use a value-identification exercise to build a prioritized funnel of potential use cases for further exploration. Make employee efficiency, productivity, and/or effectiveness part of the value formula.

Every function will have its own high-value use cases, but in manufacturing operations, we see three that have significant potential for empowering the workforce:

Reduce the time to output. PLC programming is a good example of this. Say it currently takes one day to code a PLC. With support from GenAI, which augments knowledge and quickly iterates ideas for the desired output, a programmer could produce code in 60% of the time — a 40% efficiency gain. Here, AI isn’t replacing people, but it is helping them to work faster.

Manage uncertainty better. Unanticipated scenarios often disrupt monthly, quarterly, or yearly plans. Machines break down, people don’t show up for work, or defects materialize. In the rush to get back on track, there usually isn’t time to gather and analyze all the potentially relevant information needed to make the best possible decision. AI makes it possible for users to access and analyze data from more sources, internal and external, to reach conclusions that otherwise may not have been possible — injecting a greater degree of reliability into operations. For example, a large Japanese steel manufacturer implemented AI for predictive maintenance and to optimize blast furnace operations, achieving significant cost savings, operational efficiency, and reduced unexpected downtime.

“Given the buzz around GenAI, it is particularly important to understand the differences between this form of AI and the broader concept of AI/ML.”

Knowledge retention and transfer. In West Monroe’s manufacturing poll, 95% of respondents said they worry about the impact of an aging workforce — a key concern, of course, being loss of institutional knowledge. Here again, AI, and particularly GenAI, may be useful for capturing and sharing that knowledge before it walks out the door. For example, create a simple standard root-cause analysis form and begin using it to capture data from operators every time there is an issue. You can then train an LLM to analyze that database of information, along with SOPs, best practices, and troubleshooting guides. Workers grappling with an issue can query the LLM to access relevant policies, instructions, or suggestions.

Integrate AI with Tools Familiar to Workers

One of the beneficial features of AI is the ability to integrate with other systems (relatively) easily. As a result, users can benefit from its capabilities without having to learn an entirely new tool. A worker can query a familiar interface — for example, a commercially available manufacturing intelligence/analytics platform that uses the data from the MES system — to retrain pretrained models to get customized answers for problems that are very specific and contextual to the manufacturer, or even to a specific facility.

In addition, the insights can be made available to worker in a tool familiar to the worker, thus reducing adoption challenges. Industrial co-pilots that can collected MES and other manufacturing data and then leverage the power of GenAI to provide insights in an easy-to -understand form. An example interaction would be where a plant manager can query the Co-pilot in plain language to “forecast the energy consumption of the blast furnace for the next week” and receive an easy-to-understand line chart forecast.

Shore up Your Foundation for Using AI

The idea of using AI to predict machine failure and maintenance requirements is enticing, but the reality is that most companies don’t have the essentials — including job plans or accurate data — to address common failures. Many do not routinely review maintenance procedures for specific equipment. Some don’t have documented procedures at all. The same applies to standard operating procedures. They may exist, but they may be out of date.

One of the most important foundational elements for AI is good data. Many manufacturers don’t collect or retain the data needed to benefit from AI, so that is a starting point. Some collect data, but haven’t “cleaned” it (i.e., detecting and correcting corrupt, inaccurate, or duplicate records from a database) so that analytics tools can produce useful insight. Data hygiene is mundane and laborious — but ignoring it and expecting AI to be able to overcome issues will ultimately lead to suboptimal impact. Think garbage in, garbage out.

If you have high hopes for leveraging AI to elevate performance, start by fixing these core building blocks. An easy way to think about this is cleaning up the dirty laundry that’s been building over the years. Every manufacturer has a pile of it. And every little bit of work to address it will ease the ability to employ and benefit from AI.

Don’t Underestimate the Change Management

For AI to truly become a tool that augments work and improves efficiency, the workforce must become comfortable with and understand it — including what is changing, why, and what’s in it for them. Leadership must actively dispel the myth that machines are here to replace workers. This is also a great opportunity to instill a deeper understanding of work, how individuals’ roles impact performance and how the introduction of GenAI or AI will change work. The change management plan should reflect this.

“Look for ways to begin infusing GenAI into the daily responsibilities of those doing knowledge, leadership, or decision-making work by explicitly making it part of their roles.”

Change management will require a shift in focus from training to learning, as well as new methods of delivering insight that emphasize coaching and mentoring rather than classroom education. In the MLC’s study, linked above, 65% of companies have yet to allocate specific budgets for AI training, highlighting the potential challenges for future workforce readiness.

One way of acclimating workers to change is a “quiet pilot.” For example, you can introduce a small-scale GenAI-powered “how-to” guide within an existing application. This guide can provide prompts and assistance based on the user’s role, helping them discover and use the AI tool independently. This approach introduces people to the concept of AI without making it seem like a big change. It also allows for quick learning and adjustments that can be applied to future investments.

Build GenAI into Roles

Finally, look for ways to begin infusing GenAI into the daily responsibilities of those doing knowledge, leadership, or decision-making work by explicitly making it part of their roles. While this is a recommendation for “right now” it is also encouraged in how manufacturers frame and design jobs going forward. Weaving GenAI activities into roles and responsibilities challenges managers and leaders to re-think the way work can be done. And adding “Experience leveraging GenAI in daily activities” into the knowledge, skill, and experience sections of position descriptions helps to groom the candidate pool, while exploring GenAI skills and experiences in interviewing prospective employees enables manufacture to truly begin building the workforce of the future. This combination reinforces that in most cases GenAI isn’t “a job” but rather a way of working more efficiently in many different jobs.

Take Action—and Start Adding up the Value

The Rockwell Automation State of Smart Manufacturing Report confirms what many manufacturers know: they are using a relatively low percentage (44%) of data effectively. AI can help you begin boosting this right away — and spread the impact from the executive suite down to your operational workforce. The key is focusing on how AI can augment workers’ expertise and make them more efficient and productive. This takes coordinated effort around people, processes, and technology, but the steps above will point your organization in the right direction.  M

About the authors:

Sujit Acharya is a Managing Director with West Monroe’s Technology Practice.

Randal Kenworthy is Senior Partner, Consumer and Industrial Products, with West Monroe.

Kris Slozak is Director, Consumer & Industrial Products, with West Monroe.

Glenn Pfenninger is a Director, Human Capital Management, with West Monroe.

As manufacturers move toward building smarter factories with connected machines, the data those systems produce can offer a host of benefits: improved efficiency, better productivity, informed decision-making, value creation and, ultimately, competitiveness. Yet becoming a data-driven business comes with its share of challenges. In this year’s Manufacturing in 2030 Survey, Data Mastery: A Key to Industrial Competitiveness, the NAM’s Manufacturing Leadership Council sheds light on the successes and opportunities for how manufacturers are transforming their operations with data.

Security and privacy concerns: As factories become more connected, cybersecurity becomes a greater imperative. For this reason, survey respondents validated that both data security and data privacy are essential.

• More than 90% of respondents have a formal or partial policy on data security and data privacy.
• About two-thirds of manufacturers have a formal or partial policy on data quality.
• More than 60% have a corporate-wide plan, strategy or guidelines for data management, but only 15% follow the plan in its entirety.

How data is used: As manufacturers advance along their M4.0 journey, data is becoming their lifeblood, driving insights and decision-making. Yet the survey revealed a gap between available data sources and their utilization, a notable area for improvement as the industry looks toward the future.

• Spreadsheets are still king: 70% of manufacturers enter data to them manually, and 68% still use them to analyze data.
• 44% of manufacturing leaders say the amount of data they collect is double what it was two years ago, and they anticipate it will triple by 2030.
• While nearly 60% of manufacturers use data to understand and optimize projects, there is a shift toward using data to make predictions about operational performance, including machine performance, in the next decade.

Business impact: Most manufacturers leverage data to find ways to save money or promote business growth. However, less than half have a good understanding of the dollar value of their data.

• Only about 25% of manufacturers have high confidence that the right data is being collected.
• Most manufactures have only moderate confidence in their analytic capabilities.
• Top challenges include data that comes from different systems or in different formats (53%), data that is not easy to access (28%) and lack of skills to analyze data effectively (28%).
• However, despite those challenges, 95% of manufacturers say data makes for faster and/or higher-quality decision-making.

The bottom line: An overwhelming majority of manufacturers (86%) believe that the effective use of manufacturing data will be “essential” to their competitiveness. But to realize data’s potential, manufacturers must figure out how to organize and analyze their data effectively, ensure that their data is trustworthy and align their business strategy closely with their data strategy.

Explore the survey: Get a deeper look at the current state of data mastery in manufacturing. Click here to download your copy.

The names are in! The Manufacturing Leadership Council—the NAM’s digital transformation division—is pleased to announce the winners of the 2024 Manufacturing Leadership Awards.

Now in its 20th year, the awards competition recognizes outstanding manufacturing companies and their leaders for groundbreaking use of advanced manufacturing technology.

“The class of 2024 should indeed be proud of their achievements in advancing the digital model of manufacturing,” said MLC Founder, Vice President and Executive Director David R. Brousell. “The awards reflect the truly incredible amount of innovation taking place in all sectors of the industry.”

Manufacturing Leader of the Year: Cooley Group President and CEO Daniel Dwight is the 2024 Manufacturing Leader of the Year.

• Dwight, who also serves on the MLC’s Board of Governors and is a member of the Executive Committee of the NAM Board of Directors, has overseen a significant turnaround in Cooley’s business performance through digital transformation, with a commitment to investing in smart factory technologies and developing a digital-ready workforce and business culture.
• In addition, the MLC named Cooley Group the 2024 Small/Medium Enterprise Manufacturer of the Year.

Large Enterprise Manufacturer of the Year: Intertape Polymer Group is the 2024 Large Enterprise Manufacturer of the Year.

• The award recognizes IPG’s achievements in digital transformation, including technology integration and workforce training.
• The company has also made noteworthy strides in sustainability through reductions in both energy usage and waste.

More honors: The MLC also announced winners in 11 project and individual categories, as well as the winners of the Manufacturing in 2030 Awards. The latter are given to projects with particularly forward-thinking innovations.

• The MLC honored all finalists and winners at the Manufacturing Leadership Awards Gala last night in Marco Island, Florida. A complete list of finalists and winners is available here.

Nominations for the 2025 season of the Manufacturing Leadership Awards will open on Sept. 16, 2024. More information is available here.

Marco Island, Fla. – The National Association of Manufacturers’ Manufacturing Leadership Council has named Daniel Dwight, President and CEO of Cooley Group, as the 2024 Manufacturing Leader of the Year. Dwight has overseen a significant turnaround in Cooley’s business performance through digital transformation with a commitment to investing in smart factory technologies and developing a digital-ready workforce and business culture. Dwight also serves on the MLC’s Board of Governors and is a member of the NAM Board of Directors Executive Committee. Additionally, Cooley Group was named the 2024 Small-Medium Enterprise Manufacturer of the Year.

Intertape Polymer Group was named the Large Enterprise Manufacturer of the Year in recognition of its achievements in digital transformation, including technology integration and workforce training. The company was also recognized for its noteworthy achievements in sustainability through both reductions in energy usage and waste.

“The class of 2024 should indeed be proud of their achievements in advancing the digital model of manufacturing,” said David R. Brousell, Founder, Vice President and Executive Director of the MLC. “The awards reflect the truly incredible amount of innovation taking place in all sectors of the industry.”

The 20th annual award ceremony took place at the conclusion of Rethink: Accelerating Digital Transformation in Manufacturing, the MLC’s signature event that focuses on insights and strategies for how manufacturers can further their operational digital transformation. The event took place at the JW Marrott Marco Island Beach Resort in Florida June 2-5.

“Manufacturers continually find new and inventive ways to not just bring new technology into their factories, but also how to leverage it for highly effective problem solving and even developing new processes and products that can allow for entry into new markets and new revenue streams,” said Penelope Brown, Senior Content Director and head of the MLC Awards program.

Manufacturing Leadership Award finalists and winners are determined by a distinguished panel of judges with significant industry expertise and experience. In addition to Cooley and IPG, the judges also conferred honors on the following category winners:

AI and Machine Learning
Celanese

Collaborative Ecosystems
Anheuser-Busch InBev

Digital Network Connectivity
Molex

Digital Supply Chains
The Wonderful Company

Engineering and Production Technology
Van Wijnen Smart Structures

Enterprise Integration and Technology (tie)
Dow
General Motors

Operational Excellence
Owens Corning

Sustainability and the Circular Economy
Intertape Polymer Group

Transformational Business Cultures
Humtown Products

Digital Transformation Leadership
Julian Tan, IBM

Next-Generation Leadership (tie)
Angela Accurso, MxD
Marlon Gonzalez, IBM

Manufacturing in 2030 Awards
Anheuser-Busch InBev
Cooley Group
Intel
Maxion Wheels
MxD
Nexteer Automotive

The 2025 Manufacturing Leadership Awards will open to nominations on September 16, 2024. Information about the awards program is available at https://manufacturingleadershipcouncil.com/leadership-awards/.

Five foundational areas of focus for an effective change plan 

TAKEAWAYS:
● A clear change management plan can help employees and broader teams mitigate change saturation, avoid burnout and adapt most efficiently.
● Businesses need to pay just as much attention to behavioral implications as they do technological implications of change planning and management.
● Successful implementation requires top-down communication and active stakeholder engagement early on to maximize buy-in and stewardship.   

Increasingly interconnected operations are spurring the need for broader organizational change management initiatives at many manufacturing companies. As businesses implement more advanced technologies — from robotics and data analytics to Internet of Things devices — it is critical that they also take a strategic approach to change management so their processes and people can adapt in an increasingly technology-driven environment.

A successful change management plan in the Manufacturing 4.0 era requires thinking about how to evolve the technological, people and process aspects of the change, and how those areas harmonize with each other.

While technology is central to so much of the change that manufacturers are experiencing, it does not exist in a vacuum; consumer preferences and markets are evolving, driving changes in what, where and how products are made and sold. Supply chains are shifting, compliance requirements are evolving, and decarbonization is becoming a higher priority.

Constant change and the need to adapt have become the new norm for organizations, and thus a strong change management plan is central to enabling speed to value, efficiency, innovation and resilience. Because a clear organizational change plan is about equipping the business to maximize the value it gets out of the change being implemented, ensuring stakeholder buy-in and a talent impact analysis is also key.

Developing a Plan: Five Core Areas

A common challenge in the realm of change management is employees’ capacity for constant (or near-constant) change. Teams can easily get exhausted or feel a lack of clarity around how best to prioritize. As the adage goes, if everything is critical, then nothing is critical.

“The average employee experienced 10 planned enterprise changes in the past 12 months alone, and they are getting fatigued,” according to Gartner® research published in 2023. “Willingness to support organizational change collapsed from 74% of employees in 2016 to just 43% in 2022, so it is no surprise that change fatigue is HR’s top change management concern for 2023,” Gartner found.

If a technology is increasing the speed of operations, that might seem simple, but it may still be challenging to implement in terms of how it shows up in employees’ daily work.

A clear plan for change management can help employees and broader teams mitigate that change saturation, avoid burnout and understand how to adapt most efficiently.

In its simplest form, an effective change plan has five foundational areas:

1. Assessing the baseline: At the beginning of a change management endeavor, manufacturers should determine their baseline change readiness through understanding potential risks, aligning leaders on success criteria, and defining the strategy for deploying the change.
2. Stakeholder analysis and communications: Companies need to have a detailed, robust communications plan from the start. This plan conveys how the coming changes will benefit customers, the employees and the company overall and why the business is making such changes. A stakeholder analysis should identify every internal and external role that will be affected by the change — whether employees, customers, vendors or others — and assess the impact on each. This step should address training activities, technologies and other tools people will need to adopt the change.
3. Developing a change network: It is important to identify change champions within the business — typically key leaders or influencers — who volunteer to help motivate peers about the plan. This network can help streamline the deployment of communications and training efforts and build broader cultural momentum around the plan. Successful implementation requires not only top-down communication but active stakeholder engagement early on to maximize buy-in and stewardship.
4. Conducting an impact analysis: Businesses need to understand the potential process, technology and workflow implications of coming organizational changes. An impact analysis can help map out how those areas might look in the future versus in their current state and identify areas where employees might be asked to go about their work differently. Teams can also use that analysis as a tool in training, measuring the adoption rate and reviewing success criteria.
5. Analyzing training needs and delivery strategy: Teams should use factors uncovered during the impact analysis to identify the knowledge and training needs of employees affected by the change, and how best to deliver this training. A virtual role-based curriculum may be a useful way to deliver this training.

In all these foundational areas, businesses need to pay just as much attention to the behavioral implications as they do the technological implications of change planning and management. If a technology is increasing the speed of operations, that might seem simple, but it may still be challenging to implement in terms of how it shows up in employees’ daily work.

To enable individual team members to succeed and become more agile, manufacturers should think strategically about how they envision success and then map that back to specific ways in which employee behaviors may need to change. As part of this effort, leadership teams should address the fear, uncertainty and doubt that typically accompany operational change, and welcome employee perspectives.

Measuring Success

It may seem daunting to figure out how to measure the success of a change management initiative or plan, given how all-encompassing such plans may be. Even though internal change management departments have become more common for many companies in recent decades, working with a third-party advisor who comes into the situation with a neutral viewpoint and broader perspectives can be especially helpful to gauge success and progress.

Leadership teams should address the fear, uncertainty and doubt that typically accompany operational change, and welcome employee perspectives.

A company’s success criteria must consider how workers function in the new environment and whether there is a critical mass of employees who have adapted to the new environment. Implementing a new technology or process on its own will not do much good if it is not integrated in a way that allows employees to use it efficiently and toward broader innovation.

Here are examples of questions that might help companies measure the success of the human components of their change management plan:

• If a new technology was implemented to improve inventory turns, for instance, what does it take from a human perspective to increase those turns?
• What factors are you using to determine whether you have successfully defined new roles?
• How are you measuring whether employees are proficient in new technologies?

The Forward Look

Manufacturers today rely on industrial automation and connected operations in their factories, and they cannot fully take advantage of those capabilities unless the workforce is equipped to navigate the swift pace of multi-faceted change. Companies need to be just as strategic about managing that change as they are about reaching their broader goals.  M

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

About the authors:

David Carter is a director and industrials senior analyst at RSM US LLP.

Irina Im is a senior manager and industrials senior analyst at RSM Canada.

Tom Kane is a senior director at RSM US LLP.

A major global CPG company scaled digital transformation effectively across its manufacturing network by putting people at the center. 

TAKEAWAYS:
● While change management holds the key to success in digital transformations, without buy-in from people—from the boardroom to the frontline—there won’t be any traction..
● Companies need to measure the impact of the transformation—it is critical to track KPIs to measure the success of a learning program just like they track the success of the overall transformation.
● Achieving scale in digital transformations requires thinking both globally and locally.   

Expanding Industry 4.0 efforts across highly fragmented networks remains a genuine struggle. Without enthusiastic adoption by local teams, digital transformation risks losing momentum—leaving companies at risk of failing to realize a return on investment or unlock the efficiency gains promised by Industry 4.0.

As ever, change management holds the key to success—providing the “x-factor” that maximizes technology and business value. Some companies are reimagining change management by putting people at the center of their network transformation, building critical capabilities in-house, and creating “lighthouse” plants that serve as a beacon for the at-scale capabilities the organization needs to build.

This digital manufacturing approach comprises five core elements—strong cross-site communication through central governance and a diverse local team; adopting a value-back approach so efforts are made where they really count; implementing agile “waves” with the user at the center of the framework; designing an information technology/operations technology (IT/OT) stack in parallel to allow faster scale-up in other sites; and building capabilities in model sites and across the wider organization.

This article explores how a leading global CPG company harnessed capability building as part of its transformation effort (Figure 1). The company achieved a double-digit uplift in throughput across the digital transformation of  approximately 20 business units in focus for this effort. Rarely can such results be sustained through a technology-focused approach alone.

Figure 1: The company used a structured approach to diagnose, design, launch, and deliver cohort-specific learning journeys across the network

Capability-led, from the Start

The company set the groundwork for a successful digital transformation program by establishing a digital manufacturing pilot in the largest site in its network and defining the digital operating model and IT/OT future state and rollout plan. It was ready to deliver in a digital world, but it needed its people to come on the journey.

To ensure success and to unlock the full potential of employees and tech in tandem, the company honed in on the skills, knowledge, and mindsets that aligned with how people create value for the business. Four elements made the difference: shared goals and priorities, tailored learning, agreed measures of success, and momentum post launch.

1.     Organization goals and talent: Aligning priorities

From the outset, the company built its capability-building program around a clear picture of the organization’s transformation aspirations—and the talent needed to achieve them.

The program’s objectives reflected its people-led principles: creating awareness and excitement, which meant enabling everyone to envision the “art of the possible” and how their roles could be positively impacted through digital change; building digital, automation, and analytics skills by ensuring each person had the relevant technical skills to lead, design, build, implement, or adopt new tools; and transforming ways of working by establishing a broad understanding of new processes and digital tools and supporting greater collaboration with colleagues (Figure 2).

Figure 2: The company’s comprehensive academy curriculum achieved three primary objectives

2.     A network of people: Tailoring the learning journey

To enhance the impact and scalability of its learning program, the company crafted learning journeys tailored to different needs. Instead of a one-size-fits all approach, the company defined cohorts across functions and grouped them by seniority level (Figure 3). It grouped each cohort according to the contribution they could make to the network’s digital transformation and the learning they would need to do it well.

Figure 3: Learning cohorts identified to group roles with similar digital and analytical learning needs

At the highest level, global leaders had a stake in the transformation journey and kept the program objectives in focus. They acted as champions of the overarching goal, providing clear communication that reinforced expectations and secured the resources needed to sustain the transformation journey. Advanced analytics teams learned the skills to build valuable digital and analytics solutions and the change teams brought forward the voice of the business to shape solutions and ensure their adoption.

Plant leaders and managers were role models for the new way of working and acted as influencers to sustain excitement and awareness. Associates made up the largest cohort, supporting the change and carrying it out in their on-the-ground activities.

“From the outset, the company built its capability-building program around a clear picture of the organization’s transformation aspirations—and the talent needed to achieve them.”

In practice, while global leaders focused on understanding the principles of digital and analytics, their primary learning goal was as transformation orchestrator and enabler. Meanwhile, analytics and change team members received more detailed training on specific tools and techniques, such as optimization using advanced analytics, including hands-on exercises and joining project teams to develop and implement use cases.

3.     Measurement and key performance indicators (KPIs): Planning for success

The company understood that the learners’ voice matters from the start. Scaling was front of mind in the planning phase, and this included looking at how success would be measured. The company established KPIs to track the execution of the program by looking at participation and attendance. It tracked how smoothly the program was running by monitoring progress toward operational and financial targets.

At the same time, the company considered measures to mark the impact of the intervention, including measuring the participant experience and gathering feedback on the relevance of the learning.

4.     The “steady state phase”: Maintaining momentum

The company tracked KPIs to hold teams accountable, with mechanisms to address challenges as they arose. Likewise, the company acted on post-session feedback from learners quickly using content tailored to meet cohort needs with each iteration.

Facilitators and production worked together to meet lesson delivery schedules and all participants were kept in the loop through reliable and clear communication of upcoming events. Local and regional champions helped to scale the program globally, acting as a critical link between the central transformation team and each site—tailoring content and supporting translations and local delivery.

Learning was then quickly converted into on-the-job training where employees could apply their knowledge in real scenarios with coaching through fieldwork.

How to Make the Shift

The quantifiable metrics achieved through the program are impressive:  approximately 5,000 learners participated in the program, accumulating close to 100,000 hours of learning. Employees’ enthusiastic engagement reflected the quality of the program, which achieved a recommendation rate of more than 90 percent.

All of these efforts translated into real results on the factory floor, with a double-digit uplift in throughput across the sites in scope in the factory network. The program also delivered other benefits, including talent attraction and retention.

“Learning was then quickly converted into on-the-job training where employees could apply their knowledge in real scenarios with coaching through fieldwork.”

Other companies that are ready to unlock the potential of their whole network can also create a strong learning culture by adopting these actions:

• Establish leadership support through a steering committee, dedicated resources, and sponsorship who can “speak up” for program objectives.
• Take an agile approach to allow participants to provide feedback and contribute to the continuous refinement of the program.
• Track and measure by gathering feedback, tracking KPIs, and providing incentives that foster enthusiastic learning and offer opportunities to make meaningful improvements throughout the program.
• Think globally and locally—it’s important to strike a balance between the company’s global needs and its standard operating model and the local needs of learners.
• Communicate expectations clearly by having leaders reinforce the objectives globally with the help of local influencer leaders.

Achieving digital transformation at scale is not easy, but it is doable. This company successfully scaled its digital transformation program precisely because of its people, not in spite of them.  M

About the author:

Mike Doheny is a senior partner in McKinsey’s Atlanta office, and co-leads our global Manufacturing and Supply Chain practice.

Roberto Migliorini is partner in McKinsey’s London office, and advises consumer clients on large-scale Industry 4.0 transformations.

Ewelina Gregolinska is an associate partner in McKinsey’s London office.

Justin Grover is an asset leader for McKinsey Academy in McKinsey’s Chicago office.